Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations149391
Missing cells33049
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 MiB
Average record size in memory88.0 B

Variable types

Categorical1
Numeric10

Alerts

SeriousDlqin2yrs is highly imbalanced (64.5%)Imbalance
MonthlyIncome has 29221 (19.6%) missing valuesMissing
NumberOfDependents has 3828 (2.6%) missing valuesMissing
RevolvingUtilizationOfUnsecuredLines is highly skewed (γ1 = 97.43321053)Skewed
NumberOfTime30-59DaysPastDueNotWorse is highly skewed (γ1 = 24.47460808)Skewed
DebtRatio is highly skewed (γ1 = 94.97972054)Skewed
MonthlyIncome is highly skewed (γ1 = 114.0165653)Skewed
NumberOfTimes90DaysLate is highly skewed (γ1 = 25.10737158)Skewed
NumberOfTime60-89DaysPastDueNotWorse is highly skewed (γ1 = 25.42438785)Skewed
RevolvingUtilizationOfUnsecuredLines has 10569 (7.1%) zerosZeros
NumberOfTime30-59DaysPastDueNotWorse has 125453 (84.0%) zerosZeros
DebtRatio has 3515 (2.4%) zerosZeros
MonthlyIncome has 1616 (1.1%) zerosZeros
NumberOfOpenCreditLinesAndLoans has 1712 (1.1%) zerosZeros
NumberOfTimes90DaysLate has 141108 (94.5%) zerosZeros
NumberRealEstateLoansOrLines has 55579 (37.2%) zerosZeros
NumberOfTime60-89DaysPastDueNotWorse has 141831 (94.9%) zerosZeros
NumberOfDependents has 86392 (57.8%) zerosZeros

Reproduction

Analysis started2025-10-12 00:16:16.694810
Analysis finished2025-10-12 00:16:28.644400
Duration11.95 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

SeriousDlqin2yrs
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
139382 
1
 
10009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters149391
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0139382
93.3%
110009
 
6.7%

Length

2025-10-12T05:46:28.694413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-12T05:46:28.765236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0139382
93.3%
110009
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0139382
93.3%
110009
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)149391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0139382
93.3%
110009
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)149391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0139382
93.3%
110009
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)149391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0139382
93.3%
110009
 
6.7%

RevolvingUtilizationOfUnsecuredLines
Real number (ℝ)

Skewed  Zeros 

Distinct125728
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0710867
Minimum0
Maximum50708
Zeros10569
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:28.856886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.030131807
median0.15423458
Q30.55649424
95-th percentile0.9999999
Maximum50708
Range50708
Interquartile range (IQR)0.52636244

Descriptive statistics

Standard deviation250.26367
Coefficient of variation (CV)41.22222
Kurtosis14485.675
Mean6.0710867
Median Absolute Deviation (MAD)0.14812895
Skewness97.433211
Sum906965.71
Variance62631.906
MonotonicityNot monotonic
2025-10-12T05:46:28.973961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010569
 
7.1%
0.99999999956
 
6.7%
117
 
< 0.1%
0.95009988
 
< 0.1%
0.9540918166
 
< 0.1%
0.713147416
 
< 0.1%
0.0079840326
 
< 0.1%
0.5389221565
 
< 0.1%
0.5828343315
 
< 0.1%
0.0049995
 
< 0.1%
Other values (125718)128808
86.2%
ValueCountFrequency (%)
010569
7.1%
8.37 × 10-61
 
< 0.1%
9.93 × 10-61
 
< 0.1%
1.25 × 10-51
 
< 0.1%
1.43 × 10-51
 
< 0.1%
1.49 × 10-51
 
< 0.1%
1.51 × 10-51
 
< 0.1%
1.6 × 10-51
 
< 0.1%
1.64 × 10-51
 
< 0.1%
1.87 × 10-51
 
< 0.1%
ValueCountFrequency (%)
507081
< 0.1%
291101
< 0.1%
221981
< 0.1%
220001
< 0.1%
205141
< 0.1%
183001
< 0.1%
174411
< 0.1%
139301
< 0.1%
134981
< 0.1%
134001
< 0.1%

age
Real number (ℝ)

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.306237
Minimum0
Maximum109
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:29.081873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q141
median52
Q363
95-th percentile78
Maximum109
Range109
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.725962
Coefficient of variation (CV)0.28153358
Kurtosis-0.49403919
Mean52.306237
Median Absolute Deviation (MAD)11
Skewness0.19225806
Sum7814081
Variance216.85396
MonotonicityNot monotonic
2025-10-12T05:46:29.190184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
493834
 
2.6%
483799
 
2.5%
503747
 
2.5%
473719
 
2.5%
463710
 
2.5%
633705
 
2.5%
533644
 
2.4%
513621
 
2.4%
523601
 
2.4%
563583
 
2.4%
Other values (76)112428
75.3%
ValueCountFrequency (%)
01
 
< 0.1%
21162
 
0.1%
22368
 
0.2%
23592
 
0.4%
24783
0.5%
25938
0.6%
261186
0.8%
271330
0.9%
281556
1.0%
291694
1.1%
ValueCountFrequency (%)
1092
 
< 0.1%
1071
 
< 0.1%
1051
 
< 0.1%
1033
 
< 0.1%
1023
 
< 0.1%
1013
 
< 0.1%
998
< 0.1%
986
 
< 0.1%
9717
< 0.1%
9617
< 0.1%

NumberOfTime30-59DaysPastDueNotWorse
Real number (ℝ)

Skewed  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39388584
Minimum0
Maximum98
Zeros125453
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:29.286386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.8529531
Coefficient of variation (CV)9.7819031
Kurtosis616.24563
Mean0.39388584
Median Absolute Deviation (MAD)0
Skewness24.474608
Sum58843
Variance14.845248
MonotonicityNot monotonic
2025-10-12T05:46:29.365551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0125453
84.0%
116033
 
10.7%
24598
 
3.1%
31754
 
1.2%
4747
 
0.5%
5342
 
0.2%
98220
 
0.1%
6140
 
0.1%
754
 
< 0.1%
825
 
< 0.1%
Other values (6)25
 
< 0.1%
ValueCountFrequency (%)
0125453
84.0%
116033
 
10.7%
24598
 
3.1%
31754
 
1.2%
4747
 
0.5%
5342
 
0.2%
6140
 
0.1%
754
 
< 0.1%
825
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
98220
0.1%
965
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
111
 
< 0.1%
104
 
< 0.1%
912
 
< 0.1%
825
 
< 0.1%
754
 
< 0.1%
6140
0.1%

DebtRatio
Real number (ℝ)

Skewed  Zeros 

Distinct114194
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.43674
Minimum0
Maximum329664
Zeros3515
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:29.469352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00519896
Q10.17744143
median0.36823354
Q30.87527934
95-th percentile2455.5
Maximum329664
Range329664
Interquartile range (IQR)0.69783791

Descriptive statistics

Standard deviation2041.8435
Coefficient of variation (CV)5.7608121
Kurtosis13681.588
Mean354.43674
Median Absolute Deviation (MAD)0.24529037
Skewness94.979721
Sum52949659
Variance4169124.7
MonotonicityNot monotonic
2025-10-12T05:46:29.586008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03515
 
2.4%
1227
 
0.2%
4174
 
0.1%
2170
 
0.1%
3160
 
0.1%
5143
 
0.1%
9125
 
0.1%
10117
 
0.1%
13114
 
0.1%
7114
 
0.1%
Other values (114184)144532
96.7%
ValueCountFrequency (%)
03515
2.4%
2.6 × 10-51
 
< 0.1%
3.69 × 10-51
 
< 0.1%
3.93 × 10-51
 
< 0.1%
6.62 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8 × 10-51
 
< 0.1%
8.57 × 10-51
 
< 0.1%
9.09 × 10-51
 
< 0.1%
9.15 × 10-51
 
< 0.1%
ValueCountFrequency (%)
3296641
< 0.1%
3264421
< 0.1%
3070011
< 0.1%
2205161
< 0.1%
1688351
< 0.1%
1109521
< 0.1%
1068851
< 0.1%
1013201
< 0.1%
619071
< 0.1%
61106.51
< 0.1%

MonthlyIncome
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct13594
Distinct (%)11.3%
Missing29221
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean6675.0983
Minimum0
Maximum3008750
Zeros1616
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:29.694399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1300
Q13400
median5400
Q38250
95-th percentile14594.1
Maximum3008750
Range3008750
Interquartile range (IQR)4850

Descriptive statistics

Standard deviation14389.582
Coefficient of variation (CV)2.1557109
Kurtosis19494
Mean6675.0983
Median Absolute Deviation (MAD)2312.5
Skewness114.01657
Sum8.0214656 × 108
Variance2.0706008 × 108
MonotonicityNot monotonic
2025-10-12T05:46:29.802706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50002757
 
1.8%
40002106
 
1.4%
60001934
 
1.3%
30001758
 
1.2%
01616
 
1.1%
25001549
 
1.0%
100001466
 
1.0%
35001359
 
0.9%
45001226
 
0.8%
70001223
 
0.8%
Other values (13584)103176
69.1%
(Missing)29221
 
19.6%
ValueCountFrequency (%)
01616
1.1%
1596
 
0.4%
26
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
91
 
< 0.1%
102
 
< 0.1%
111
 
< 0.1%
151
 
< 0.1%
ValueCountFrequency (%)
30087501
< 0.1%
17940601
< 0.1%
15601001
< 0.1%
10725001
< 0.1%
8350401
< 0.1%
7304831
< 0.1%
7025001
< 0.1%
6995301
< 0.1%
6495871
< 0.1%
6290001
< 0.1%

NumberOfOpenCreditLinesAndLoans
Real number (ℝ)

Zeros 

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4808924
Minimum0
Maximum58
Zeros1712
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:29.910996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile18
Maximum58
Range58
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1365146
Coefficient of variation (CV)0.60565732
Kurtosis3.1172815
Mean8.4808924
Median Absolute Deviation (MAD)3
Skewness1.2218338
Sum1266969
Variance26.383782
MonotonicityNot monotonic
2025-10-12T05:46:30.015248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
613607
 
9.1%
713242
 
8.9%
512916
 
8.6%
812559
 
8.4%
411566
 
7.7%
911355
 
7.6%
109623
 
6.4%
38992
 
6.0%
118321
 
5.6%
127005
 
4.7%
Other values (48)40205
26.9%
ValueCountFrequency (%)
01712
 
1.1%
14251
 
2.8%
26558
4.4%
38992
6.0%
411566
7.7%
512916
8.6%
613607
9.1%
713242
8.9%
812559
8.4%
911355
7.6%
ValueCountFrequency (%)
581
 
< 0.1%
572
 
< 0.1%
562
 
< 0.1%
544
< 0.1%
531
 
< 0.1%
523
< 0.1%
512
 
< 0.1%
502
 
< 0.1%
494
< 0.1%
486
< 0.1%

NumberOfTimes90DaysLate
Real number (ℝ)

Skewed  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2381201
Minimum0
Maximum98
Zeros141108
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:30.223511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.8261647
Coefficient of variation (CV)16.068214
Kurtosis637.85235
Mean0.2381201
Median Absolute Deviation (MAD)0
Skewness25.107372
Sum35573
Variance14.639536
MonotonicityNot monotonic
2025-10-12T05:46:30.306883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0141108
94.5%
15232
 
3.5%
21555
 
1.0%
3667
 
0.4%
4291
 
0.2%
98220
 
0.1%
5131
 
0.1%
680
 
0.1%
738
 
< 0.1%
821
 
< 0.1%
Other values (9)48
 
< 0.1%
ValueCountFrequency (%)
0141108
94.5%
15232
 
3.5%
21555
 
1.0%
3667
 
0.4%
4291
 
0.2%
5131
 
0.1%
680
 
0.1%
738
 
< 0.1%
821
 
< 0.1%
919
 
< 0.1%
ValueCountFrequency (%)
98220
0.1%
965
 
< 0.1%
171
 
< 0.1%
152
 
< 0.1%
142
 
< 0.1%
134
 
< 0.1%
122
 
< 0.1%
115
 
< 0.1%
108
 
< 0.1%
919
 
< 0.1%

NumberRealEstateLoansOrLines
Real number (ℝ)

Zeros 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0223909
Minimum0
Maximum54
Zeros55579
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:30.394434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum54
Range54
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1301955
Coefficient of variation (CV)1.1054436
Kurtosis60.585918
Mean1.0223909
Median Absolute Deviation (MAD)1
Skewness3.4847049
Sum152736
Variance1.2773419
MonotonicityNot monotonic
2025-10-12T05:46:30.494421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
055579
37.2%
152338
35.0%
231522
21.1%
36300
 
4.2%
42170
 
1.5%
5689
 
0.5%
6320
 
0.2%
7171
 
0.1%
893
 
0.1%
978
 
0.1%
Other values (18)131
 
0.1%
ValueCountFrequency (%)
055579
37.2%
152338
35.0%
231522
21.1%
36300
 
4.2%
42170
 
1.5%
5689
 
0.5%
6320
 
0.2%
7171
 
0.1%
893
 
0.1%
978
 
0.1%
ValueCountFrequency (%)
541
 
< 0.1%
321
 
< 0.1%
291
 
< 0.1%
261
 
< 0.1%
253
< 0.1%
232
< 0.1%
211
 
< 0.1%
202
< 0.1%
192
< 0.1%
182
< 0.1%

NumberOfTime60-89DaysPastDueNotWorse
Real number (ℝ)

Skewed  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21250276
Minimum0
Maximum98
Zeros141831
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:30.581843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.8105233
Coefficient of variation (CV)17.931641
Kurtosis649.09339
Mean0.21250276
Median Absolute Deviation (MAD)0
Skewness25.424388
Sum31746
Variance14.520088
MonotonicityNot monotonic
2025-10-12T05:46:30.665251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0141831
94.9%
15731
 
3.8%
21118
 
0.7%
3318
 
0.2%
98220
 
0.1%
4105
 
0.1%
534
 
< 0.1%
616
 
< 0.1%
79
 
< 0.1%
965
 
< 0.1%
Other values (3)4
 
< 0.1%
ValueCountFrequency (%)
0141831
94.9%
15731
 
3.8%
21118
 
0.7%
3318
 
0.2%
4105
 
0.1%
534
 
< 0.1%
616
 
< 0.1%
79
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
98220
0.1%
965
 
< 0.1%
111
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
79
 
< 0.1%
616
 
< 0.1%
534
 
< 0.1%
4105
 
0.1%
3318
0.2%

NumberOfDependents
Real number (ℝ)

Missing  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing3828
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean0.75986343
Minimum0
Maximum20
Zeros86392
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-10-12T05:46:30.744433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1161415
Coefficient of variation (CV)1.4688711
Kurtosis2.9842881
Mean0.75986343
Median Absolute Deviation (MAD)0
Skewness1.5833082
Sum110608
Variance1.2457717
MonotonicityNot monotonic
2025-10-12T05:46:30.828083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
086392
57.8%
126314
 
17.6%
219521
 
13.1%
39483
 
6.3%
42862
 
1.9%
5746
 
0.5%
6158
 
0.1%
751
 
< 0.1%
824
 
< 0.1%
105
 
< 0.1%
Other values (3)7
 
< 0.1%
(Missing)3828
 
2.6%
ValueCountFrequency (%)
086392
57.8%
126314
 
17.6%
219521
 
13.1%
39483
 
6.3%
42862
 
1.9%
5746
 
0.5%
6158
 
0.1%
751
 
< 0.1%
824
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
131
 
< 0.1%
105
 
< 0.1%
95
 
< 0.1%
824
 
< 0.1%
751
 
< 0.1%
6158
 
0.1%
5746
 
0.5%
42862
 
1.9%
39483
6.3%

Interactions

2025-10-12T05:46:27.013098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.252815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.215286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.161070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.127846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.098595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.136084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.117560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.073609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.041852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.112090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.344526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.307031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.252836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.223646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.186113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.236104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.217276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.165224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.144417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.209930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.440559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.402785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.359405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.319453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.277751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.331940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.307923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.256911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.236154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.315259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.536108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.498929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.456939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.419473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.365283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.427777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.406987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.352719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.331941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.416287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.623609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.590210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.544373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.506971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.461985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.519427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.498591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.436144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.423675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.671417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.727738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.686036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.640292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.611135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.556902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.619421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.602775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.540190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.520041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.769404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.823633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.783977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.736116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.702823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.648570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.715267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.698591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.640555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.619453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.863083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:18.919387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.873603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.832357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.802767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.857335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.806956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.786072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.740989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.715290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:27.957905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.015229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.965246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.927766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.898634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.944417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.902762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.877798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.840689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.825590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:28.046502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:19.111139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:20.061152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:21.027724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:22.002812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:23.031857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.007736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:24.973623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:25.936124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:46:26.917284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-10-12T05:46:30.902710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
DebtRatioMonthlyIncomeNumberOfDependentsNumberOfOpenCreditLinesAndLoansNumberOfTime30-59DaysPastDueNotWorseNumberOfTime60-89DaysPastDueNotWorseNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesRevolvingUtilizationOfUnsecuredLinesSeriousDlqin2yrsage
DebtRatio1.000-0.133-0.0420.2190.0370.002-0.0310.3960.0770.0000.029
MonthlyIncome-0.1331.0000.2040.310-0.015-0.053-0.0890.390-0.0780.0000.133
NumberOfDependents-0.0420.2041.0000.0960.0700.0360.0310.1630.1180.031-0.229
NumberOfOpenCreditLinesAndLoans0.2190.3100.0961.0000.063-0.047-0.1340.469-0.0860.0510.158
NumberOfTime30-59DaysPastDueNotWorse0.037-0.0150.0700.0631.0000.2770.2500.0210.2340.086-0.094
NumberOfTime60-89DaysPastDueNotWorse0.002-0.0530.036-0.0470.2771.0000.317-0.0440.1870.084-0.084
NumberOfTimes90DaysLate-0.031-0.0890.031-0.1340.2500.3171.000-0.1010.2370.087-0.102
NumberRealEstateLoansOrLines0.3960.3900.1630.4690.021-0.044-0.1011.000-0.0280.0330.054
RevolvingUtilizationOfUnsecuredLines0.077-0.0780.118-0.0860.2340.1870.237-0.0281.0000.000-0.277
SeriousDlqin2yrs0.0000.0000.0310.0510.0860.0840.0870.0330.0001.0000.116
age0.0290.133-0.2290.158-0.094-0.084-0.1020.054-0.2770.1161.000

Missing values

2025-10-12T05:46:28.156909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-12T05:46:28.336013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-12T05:46:28.568266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SeriousDlqin2yrsRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependents
000.9571514000.1218762600.040001.0
100.6581803810.0851133042.021000.0
200.2338103000.0360503300.050000.0
300.9072394910.02492663588.070100.0
400.2131797400.3756073500.030101.0
500.3056825705710.000000NaN80300.0
600.7544643900.2099403500.080000.0
700.11695127046.000000NaN2000NaN
800.1891695700.60629123684.090402.0
900.6442263000.3094762500.050000.0
SeriousDlqin2yrsRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependents
14938110.5521575500.3370351800.071001.0
14938210.6736996213653.000000NaN160101.0
14938310.6773453830.24227813500.090103.0
14938410.847041470432.000000NaN60000.0
14938510.1497642800.3034323700.0120100.0
14938611.000000460170.398010401.032002.0
14938711.1355524120.8458877500.0120410.0
14938810.9201073110.1767321125.041000.0
14938910.9838255500.0641164600.021006.0
14939010.2247115500.0572358700.070000.0